Könyv MULTI AGENT REINFORCEMENT LEARNING ALBRECHT STEFANO V

MULTI AGENT REINFORCEMENT LEARNING

Nyelv: Angol
Kötés: Kemény kötésű
Kiadó: MIT
Elérhetőség: Beszállítói készleten alacsony példányszámban
Küldés 11-15 napon belül
28 792 Ft
The first comprehensive introduction to Multi-Agent Reinforcement Learning (MARL), covering MARL’s m...

Információk a könyvről

Nyelv
Angol
Kötés
Könyv - Kemény kötésű
Kiadva
2024
oldal
394
EAN
9780262049375
Enbook ID
45775744
Kiadó
MIT
Súly
760

Teljes leírás

The first comprehensive introduction to Multi-Agent Reinforcement Learning (MARL), covering MARL’s models, solution concepts, algorithmic ideas, technical challenges, and modern approaches.

Multi-Agent Reinforcement Learning (MARL), an area of machine learning in which a collective of agents learn to optimally interact in a shared environment, boasts a growing array of applications in modern life, from autonomous driving and multi-robot factories to automated trading and energy network management. This text provides a lucid and rigorous introduction to the models, solution concepts, algorithmic ideas, technical challenges, and modern approaches in MARL. The book first introduces the field’s foundations, including basics of reinforcement learning theory and algorithms, interactive game models, different solution concepts for games, and the algorithmic ideas underpinning MARL research. It then details contemporary MARL algorithms which leverage deep learning techniques, covering ideas such as centralized training with decentralized execution, value decomposition, parameter sharing, and self-play. The book comes with its own MARL codebase written in Python, containing implementations of MARL algorithms that are self-contained and easy to read. Technical content is explained in easy-to-understand language and illustrated with extensive examples, illuminating MARL for newcomers while offering high-level insights for more advanced readers.

  • First textbook to introduce the foundations and applications of MARL, written by experts in the field
  • Integrates reinforcement learning, deep learning, and game theory
  • Practical focus covers considerations for running experiments and describes environments for testing MARL algorithms
  • Explains complex concepts in clear and simple language
  • Classroom-tested, accessible approach suitable for graduate students and professionals across computer science, artificial intelligence, and robotics
  • Resources include code and slides

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